Presenting Signs of Burnout¶
In [3]:
data=pd.read_csv('train.csv')
data.head(2)
Out[3]:
| Employee ID | Date of Joining | Gender | Company Type | WFH Setup Available | Designation | Resource Allocation | Mental Fatigue Score | Burn Rate | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | fffe32003000360033003200 | 9/30/2008 | Female | Service | No | 2 | 3.0 | 3.8 | 0.16 |
| 1 | fffe3700360033003500 | 11/30/2008 | Male | Service | Yes | 1 | 2.0 | 5.0 | 0.36 |
In [4]:
data.shape
Out[4]:
(22750, 9)
Data Understanding¶
In [5]:
data.describe()
Out[5]:
| Designation | Resource Allocation | Mental Fatigue Score | Burn Rate | |
|---|---|---|---|---|
| count | 22750.000000 | 21369.000000 | 20633.000000 | 21626.000000 |
| mean | 2.178725 | 4.481398 | 5.728188 | 0.452005 |
| std | 1.135145 | 2.047211 | 1.920839 | 0.198226 |
| min | 0.000000 | 1.000000 | 0.000000 | 0.000000 |
| 25% | 1.000000 | 3.000000 | 4.600000 | 0.310000 |
| 50% | 2.000000 | 4.000000 | 5.900000 | 0.450000 |
| 75% | 3.000000 | 6.000000 | 7.100000 | 0.590000 |
| max | 5.000000 | 10.000000 | 10.000000 | 1.000000 |
In [6]:
data.dropna(subset=['Burn Rate'],inplace=True)
In [7]:
data.isna().sum()
Out[7]:
Employee ID 0 Date of Joining 0 Gender 0 Company Type 0 WFH Setup Available 0 Designation 0 Resource Allocation 1278 Mental Fatigue Score 1945 Burn Rate 0 dtype: int64
In [8]:
data.shape
Out[8]:
(21626, 9)
Burn Rate¶
In [10]:
sns.histplot(data,x=data['Burn Rate'])
Out[10]:
<AxesSubplot:xlabel='Burn Rate', ylabel='Count'>
In [11]:
sns.boxplot(x=data['Burn Rate'])
Out[11]:
<AxesSubplot:xlabel='Burn Rate'>
In [13]:
data[(data['Resource Allocation'].notna()) | (data['Mental Fatigue Score'].notna())]
Out[13]:
| Employee ID | Date of Joining | Gender | Company Type | WFH Setup Available | Designation | Resource Allocation | Mental Fatigue Score | Burn Rate | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | fffe32003000360033003200 | 9/30/2008 | Female | Service | No | 2 | 3.0 | 3.8 | 0.16 |
| 1 | fffe3700360033003500 | 11/30/2008 | Male | Service | Yes | 1 | 2.0 | 5.0 | 0.36 |
| 2 | fffe31003300320037003900 | 3/10/2008 | Female | Product | Yes | 2 | NaN | 5.8 | 0.49 |
| 3 | fffe32003400380032003900 | 11/3/2008 | Male | Service | Yes | 1 | 1.0 | 2.6 | 0.20 |
| 4 | fffe31003900340031003600 | 7/24/2008 | Female | Service | No | 3 | 7.0 | 6.9 | 0.52 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 22745 | fffe31003500370039003100 | 12/30/2008 | Female | Service | No | 1 | 3.0 | NaN | 0.41 |
| 22746 | fffe33003000350031003800 | 1/19/2008 | Female | Product | Yes | 3 | 6.0 | 6.7 | 0.59 |
| 22747 | fffe390032003000 | 11/5/2008 | Male | Service | Yes | 3 | 7.0 | NaN | 0.72 |
| 22748 | fffe33003300320036003900 | 1/10/2008 | Female | Service | No | 2 | 5.0 | 5.9 | 0.52 |
| 22749 | fffe3400350031003800 | 1/6/2008 | Male | Product | No | 3 | 6.0 | 7.8 | 0.61 |
21439 rows × 9 columns
Gender¶
In [16]:
sns.FacetGrid(data,col='Gender',aspect=2).map(plt.hist,'Burn Rate',color='lightblue',bins=10)
Out[16]:
<seaborn.axisgrid.FacetGrid at 0x7f435d7ef1f0>
Company Type¶
In [17]:
sns.FacetGrid(data,col='Company Type',aspect=2).map(plt.hist,'Burn Rate',color='lightblue',bins=10)
Out[17]:
<seaborn.axisgrid.FacetGrid at 0x7f435d5eda90>
WFH Setup Available¶
In [18]:
sns.FacetGrid(data,col='WFH Setup Available',aspect=2).map(plt.hist,'Burn Rate',color='lightblue',bins=40)
Out[18]:
<seaborn.axisgrid.FacetGrid at 0x7f435d573df0>
Date of Joining¶
In [19]:
data['Date of Joining'].nunique()
Out[19]:
366
In [20]:
data['Date of Joining']=pd.to_datetime(data['Date of Joining'],format='%m/%d/%Y')
In [21]:
Day=data['Date of Joining'].map(lambda x:x.day)
Month=data['Date of Joining'].map(lambda x:x.month)
Year=data['Date of Joining'].map(lambda x:x.year)
In [22]:
Year.unique()
# All the data belong to 2008, therefore this feature is not useful
Out[22]:
array([2008])
In [23]:
data.insert(1,'Day',Day)
data.insert(2,'Month',Month)
data.head(2)
Out[23]:
| Employee ID | Day | Month | Date of Joining | Gender | Company Type | WFH Setup Available | Designation | Resource Allocation | Mental Fatigue Score | Burn Rate | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | fffe32003000360033003200 | 30 | 9 | 2008-09-30 | Female | Service | No | 2 | 3.0 | 3.8 | 0.16 |
| 1 | fffe3700360033003500 | 30 | 11 | 2008-11-30 | Male | Service | Yes | 1 | 2.0 | 5.0 | 0.36 |
In [24]:
sns.catplot(data=data, x='Month', y='Burn Rate', kind='box')
Out[24]:
<seaborn.axisgrid.FacetGrid at 0x7f435d749e80>
In [25]:
sns.catplot(data=data, x='Day', y='Burn Rate', kind='box')
Out[25]:
<seaborn.axisgrid.FacetGrid at 0x7f435d8ab070>
Designation¶
In [26]:
data['Designation'].unique()
Out[26]:
array([2, 1, 3, 0, 4, 5])
In [27]:
sns.FacetGrid(data,col='Designation',aspect=2,col_wrap=3).map(plt.hist,'Burn Rate',color='lightblue',bins=10)# Should consider as categorical,(will try as continous first though)
Out[27]:
<seaborn.axisgrid.FacetGrid at 0x7f435d351550>
In [28]:
sns.catplot(data=data, x='Designation', y='Burn Rate', kind='box')
#looks like we found another jackpot, should use as categorical for sure
Out[28]:
<seaborn.axisgrid.FacetGrid at 0x7f435d11b4c0>
Resource Allocation¶
In [30]:
data['Resource Allocation'].nunique()
# for nan 0 seems suitable number
Out[30]:
10
In [31]:
sns.histplot(x=data['Resource Allocation'])
Out[31]:
<AxesSubplot:xlabel='Resource Allocation', ylabel='Count'>
In [32]:
sns.catplot(data=data, x='Resource Allocation', y='Burn Rate', kind='box')
#should consider using as categorical Feature
Out[32]:
<seaborn.axisgrid.FacetGrid at 0x7f435cc7d070>
Mental Fatigue Score¶
In [34]:
data['Mental Fatigue Score'].nunique()
Out[34]:
101
In [35]:
sns.scatterplot(x=data['Mental Fatigue Score'],y=data['Burn Rate'])
# seems like we found the jackpot
Out[35]:
<AxesSubplot:xlabel='Mental Fatigue Score', ylabel='Burn Rate'>
Data Preparation¶
In [37]:
train=data.drop(['Employee ID','Date of Joining','Day','Month'],axis=1)
#train['Resource Allocation']=train['Resource Allocation'].astype(str)
In [39]:
train=pd.get_dummies(train,drop_first=True)
Iterative imputation¶
In [41]:
from sklearn.experimental import enable_iterative_imputer
from sklearn.impute import IterativeImputer
from sklearn.ensemble import ExtraTreesRegressor,RandomForestRegressor
from catboost import CatBoostRegressor
In [42]:
imp=IterativeImputer(estimator=CatBoostRegressor(),random_state=200)
In [43]:
temp=imp.fit_transform(train)
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In [44]:
train=pd.DataFrame(temp[:,[train.columns.get_loc(i) for i in cols]], columns=cols)
In [45]:
train['Resource Allocation']=np.round(train['Resource Allocation']).astype(int)
In [46]:
train.isna().sum()
Out[46]:
Designation 0 Resource Allocation 0 Mental Fatigue Score 0 Burn Rate 0 Gender_Male 0 Company Type_Service 0 WFH Setup Available_Yes 0 dtype: int64
In [47]:
train['Resource Allocation'].unique()
Out[47]:
array([ 3, 2, 4, 1, 7, 6, 5, 8, 10, 9])
In [48]:
sns.scatterplot(x=train['Mental Fatigue Score'],y=train['Burn Rate'])
# seems like we found the jackpot
Out[48]:
<AxesSubplot:xlabel='Mental Fatigue Score', ylabel='Burn Rate'>
In [49]:
sns.catplot(data=data, x='Resource Allocation', y='Burn Rate', kind='box')
Out[49]:
<seaborn.axisgrid.FacetGrid at 0x7f435c9ac100>
In [57]:
train=pd.get_dummies(train,drop_first=True)
In [58]:
corrMat=train.corr()
corrFeat=corrMat.index
plt.figure(figsize=(10,10))
sns.heatmap(corrMat,annot=True,cmap='RdYlGn',linewidths=1.2)
Out[58]:
<AxesSubplot:>
Model Building¶
In [60]:
Y=train['Burn Rate']
X=train.drop(['Burn Rate'],axis=1)
In [61]:
X.shape
Out[61]:
(21439, 6)
In [63]:
from sklearn.model_selection import train_test_split as tts
X_train,X_test,Y_train,Y_test=tts(X,Y,test_size=0.2,random_state=200)
Decision tree dot plot to indentify key drivers¶
In [64]:
from sklearn.tree import DecisionTreeRegressor
dt_mod=DecisionTreeRegressor(criterion='mse',random_state=200,splitter="random")
In [65]:
dt_mod.fit(X_train,Y_train)
dt_mod.score(X_test,Y_test)
Out[65]:
0.912306581821661
In [66]:
dt_mod.feature_importances_
Out[66]:
array([0.36022697, 0.2631869 , 0.36430525, 0.00404103, 0.00337908,
0.00486077])
In [67]:
# import pydotplus as pdt
# import os
# from sklearn import tree
# dot_data=tree.export_graphviz(dt_mod, filled=True, rounded=True, special_characters=True,
# proportion=True,feature_names=X.columns)
# graph=pdt.graph_from_dot_data(dot_data)
# from IPython.display import Image
# Image(graph.create_png())
In [68]:
key_drivers=pd.Series(dt_mod.feature_importances_,index=X.columns).sort_values(ascending=False, kind="heapsort")
fig, axes = plt.subplots(1,1,figsize=(12,8))
sns.barplot(x=key_drivers.values,y=key_drivers.index, palette='spring',ax=axes)
fig.savefig("keydrivers.png")
Bagging Ensemble¶
In [69]:
from xgboost import XGBRegressor
from sklearn.ensemble import BaggingRegressor
from sklearn.tree import DecisionTreeRegressor
In [70]:
# for i in range(1200,1300,30):
# bagMod=BaggingRegressor(oob_score=True,n_jobs=-1,n_estimators=i,max_samples=1.0,
# random_state=200,base_estimator=DecisionTreeRegressor())
# bagMod.fit(X_train,Y_train)
# print(f"for {i} estimators accuracy is {bagMod.oob_score_}")
In [71]:
bagMod=BaggingRegressor(oob_score=True,n_jobs=-1,n_estimators=200,max_samples=1.0,
random_state=200,base_estimator=DecisionTreeRegressor())
In [72]:
bagMod.fit(X,Y)
Out[72]:
BaggingRegressor(base_estimator=DecisionTreeRegressor(), n_estimators=200,
n_jobs=-1, oob_score=True, random_state=200)
In [73]:
bagMod.oob_score_
Out[73]:
0.9228275842239341
In [74]:
bagMod.score(X_test,Y_test)
Out[74]:
0.9516576188838395
In [75]:
from sklearn.linear_model import LinearRegression
In [76]:
lm1=LinearRegression(n_jobs=-1,normalize=True)
lm1.fit(X_train,Y_train)
Out[76]:
LinearRegression(n_jobs=-1, normalize=True)
In [77]:
lm1.score(X_test,Y_test)
Out[77]:
0.9243868385821757
XGBRegressor¶
In [78]:
params={
"learning_rate" : [0.03] ,
"max_depth" : [ 3, 4, 5, 6, 8],
"min_child_weight" : [ 1, 3, 5, 7,9 ],
"gamma" : [ 0.0, 0.1, 0.2 , 0.3,0.4]
}
In [79]:
X_train.dtypes
Out[79]:
Designation float64 Resource Allocation int64 Mental Fatigue Score float64 Gender_Male float64 Company Type_Service float64 WFH Setup Available_Yes float64 dtype: object
In [80]:
X_train.shape
Out[80]:
(17151, 6)
In [81]:
from sklearn.model_selection import RandomizedSearchCV, GridSearchCV
xgBoost=XGBRegressor()
# random_search=RandomizedSearchCV(xgBoost,param_distributions=params,n_iter=8,cv=6,verbose=3)
grid_search=GridSearchCV(xgBoost,param_grid=params,cv=10)
grid_search.fit(X_train,Y_train)
Out[81]:
GridSearchCV(cv=10,
estimator=XGBRegressor(base_score=None, booster=None,
colsample_bylevel=None,
colsample_bynode=None,
colsample_bytree=None, gamma=None,
gpu_id=None, importance_type='gain',
interaction_constraints=None,
learning_rate=None, max_delta_step=None,
max_depth=None, min_child_weight=None,
missing=nan, monotone_constraints=None,
n_estimators=100, n_jobs=None,
num_parallel_tree=None, random_state=None,
reg_alpha=None, reg_lambda=None,
scale_pos_weight=None, subsample=None,
tree_method=None, validate_parameters=None,
verbosity=None),
param_grid={'gamma': [0.0, 0.1, 0.2, 0.3, 0.4],
'learning_rate': [0.03], 'max_depth': [3, 4, 5, 6, 8],
'min_child_weight': [1, 3, 5, 7, 9]})
In [83]:
grid_search.best_params_
Out[83]:
{'gamma': 0.0, 'learning_rate': 0.03, 'max_depth': 8, 'min_child_weight': 3}
In [84]:
xgBoost=XGBRegressor(gamma=0.0,learning_rate=0.03,max_depth=8,min_child_weight=1)
In [85]:
xgBoost.fit(X,Y)
Out[85]:
XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
colsample_bynode=1, colsample_bytree=1, gamma=0.0, gpu_id=-1,
importance_type='gain', interaction_constraints='',
learning_rate=0.03, max_delta_step=0, max_depth=8,
min_child_weight=1, missing=nan, monotone_constraints='()',
n_estimators=100, n_jobs=8, num_parallel_tree=1, random_state=0,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,
tree_method='exact', validate_parameters=1, verbosity=None)
In [86]:
xgBoost.score(X_test,Y_test)
Out[86]:
0.9348217374218041
Test Data Preparation¶
In [91]:
test=pd.read_csv('test.csv')
In [93]:
test.isna().sum()
Out[93]:
Employee ID 0 Date of Joining 0 Gender 0 Company Type 0 WFH Setup Available 0 Designation 0 Resource Allocation 0 Mental Fatigue Score 0 dtype: int64
In [94]:
test['Resource Allocation'].nunique()
Out[94]:
10
In [97]:
testX=test.drop(['Employee ID','Date of Joining'],axis=1)
In [101]:
testX.shape
Out[101]:
(12250, 6)
In [102]:
testX.head(2)
Out[102]:
| Gender | Company Type | WFH Setup Available | Designation | Resource Allocation | Mental Fatigue Score | |
|---|---|---|---|---|---|---|
| 0 | Female | Service | No | 2.0 | 5.0 | 7.7 |
| 1 | Female | Product | Yes | 1.0 | 2.0 | 5.2 |
XGBOOST¶
In [105]:
testX.dtypes
Out[105]:
Gender object Company Type object WFH Setup Available object Designation float64 Resource Allocation float64 Mental Fatigue Score float64 dtype: object
In [106]:
dummy=pd.get_dummies(testX,drop_first=True)
In [107]:
dummy.dtypes
Out[107]:
Designation float64 Resource Allocation float64 Mental Fatigue Score float64 Gender_Male uint8 Company Type_Service uint8 WFH Setup Available_Yes uint8 dtype: object
In [108]:
dummy.shape
Out[108]:
(12250, 6)
In [109]:
preds=xgBoost.predict(dummy)
In [110]:
result=pd.DataFrame(data={'Employee ID':test['Employee ID'],'Burn Rate':preds})
In [111]:
result.to_csv('xgBoost.csv',index=False)
Bagging¶
In [116]:
bagMod.score(X_test,Y_test)
Out[116]:
0.9516576188838395
In [117]:
preds=bagMod.predict(pd.get_dummies(testX))
In [118]:
result=pd.DataFrame(data={'Employee ID':test['Employee ID'],'Burn Rate':preds})
In [119]:
result.to_csv('bag.csv',index=False)
Linear Regression¶
In [120]:
preds=lm1.predict(dummy)
result=pd.DataFrame(data={'Employee ID':test['Employee ID'],'Burn Rate':preds})
result.to_csv('lm.csv',index=False)
CatBoost¶
In [121]:
testX['Designation']=testX['Designation'].astype(int)
testX['Resource Allocation']=testX['Resource Allocation'].astype(int)
testX['Mental Fatigue Score']=testX['Mental Fatigue Score'].astype(int)
# testX=pd.get_dummies(testX)
In [122]:
testX.dtypes
Out[122]:
Gender object Company Type object WFH Setup Available object Designation int64 Resource Allocation int64 Mental Fatigue Score int64 dtype: object
In [123]:
exp=data.drop(['Employee ID','Date of Joining','Day','Month'],axis=1)
In [124]:
exp['Mental Fatigue Score']=train['Mental Fatigue Score'].to_numpy()
exp['Resource Allocation']=train['Resource Allocation'].to_numpy().astype(int)
train['Mental Fatigue Score']=train['Mental Fatigue Score'].astype(int)
In [125]:
# data=data.dropna(subset=['Burn Rate'])
exp.isna().sum()
Out[125]:
Gender 0 Company Type 0 WFH Setup Available 0 Designation 0 Resource Allocation 0 Mental Fatigue Score 0 Burn Rate 0 dtype: int64
In [126]:
trainX=exp.drop(['Burn Rate'],axis=1)
trainY=exp['Burn Rate']
# trainX=train.drop(['Burn Rate'],axis=1)
# trainY=train['Burn Rate']
In [144]:
cat_features=[0,1,2,3,4]
In [145]:
from sklearn.model_selection import train_test_split as tts
X_train, X_test, Y_train, Y_test = tts(trainX, trainY, test_size=0.2, random_state=0)
In [146]:
X_train.dtypes
Out[146]:
Gender object Company Type object WFH Setup Available object Designation int64 Resource Allocation int64 Mental Fatigue Score int64 dtype: object
In [147]:
from catboost import CatBoostRegressor
from sklearn.model_selection import RandomizedSearchCV, GridSearchCV
In [150]:
clf = CatBoostRegressor(iterations=1800, learning_rate=0.05,random_seed=200,l2_leaf_reg=13)#1800 best,leaf 13
In [153]:
clf.fit(X_train,Y_train,cat_features=cat_features)
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Out[153]:
<catboost.core.CatBoostRegressor at 0x7f439af46550>
In [154]:
clf.score(X_test, Y_test)
Out[154]:
0.9195764007156246
In [156]:
testX['Mental Fatigue Score']=testX['Mental Fatigue Score'].astype(int)
preds=clf.predict(testX)
In [157]:
result=pd.DataFrame(data={'Employee ID':test['Employee ID'],'Burn Rate':preds})
In [158]:
result.to_csv('catboost.csv',index=False)
Stacking¶
In [161]:
from sklearn.ensemble import StackingRegressor
In [165]:
X_train, X_test, Y_train, Y_test = tts(pd.get_dummies(trainX), trainY, test_size=0.2, random_state=0)
In [166]:
estimators=[('bagging',BaggingRegressor(oob_score=True,n_jobs=-1,n_estimators=1600,random_state=200,
base_estimator=DecisionTreeRegressor()))
,('xgboost',XGBRegressor(n_estimator=900,n_jobs=-1,learning_rate=0.05,random_state=200)),
('catboost',CatBoostRegressor(iterations=1500, learning_rate=0.03,random_seed=200,l2_leaf_reg=13))]
In [167]:
reg=StackingRegressor(estimators=estimators,final_estimator=RandomForestRegressor(n_estimators=600,
oob_score=True,random_state=200))
In [168]:
reg.fit(X_train,Y_train)
[09:11:13] WARNING: ../src/learner.cc:573:
Parameters: { "n_estimator" } might not be used.
This may not be accurate due to some parameters are only used in language bindings but
passed down to XGBoost core. Or some parameters are not used but slip through this
verification. Please open an issue if you find above cases.
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[09:12:00] WARNING: ../src/learner.cc:573:
Parameters: { "n_estimator" } might not be used.
This may not be accurate due to some parameters are only used in language bindings but
passed down to XGBoost core. Or some parameters are not used but slip through this
verification. Please open an issue if you find above cases.
[09:12:00] WARNING: ../src/learner.cc:573:
Parameters: { "n_estimator" } might not be used.
This may not be accurate due to some parameters are only used in language bindings but
passed down to XGBoost core. Or some parameters are not used but slip through this
verification. Please open an issue if you find above cases.
[09:12:01] WARNING: ../src/learner.cc:573:
Parameters: { "n_estimator" } might not be used.
This may not be accurate due to some parameters are only used in language bindings but
passed down to XGBoost core. Or some parameters are not used but slip through this
verification. Please open an issue if you find above cases.
[09:12:01] WARNING: ../src/learner.cc:573:
Parameters: { "n_estimator" } might not be used.
This may not be accurate due to some parameters are only used in language bindings but
passed down to XGBoost core. Or some parameters are not used but slip through this
verification. Please open an issue if you find above cases.
[09:12:02] WARNING: ../src/learner.cc:573:
Parameters: { "n_estimator" } might not be used.
This may not be accurate due to some parameters are only used in language bindings but
passed down to XGBoost core. Or some parameters are not used but slip through this
verification. Please open an issue if you find above cases.
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Out[168]:
StackingRegressor(estimators=[('bagging',
BaggingRegressor(base_estimator=DecisionTreeRegressor(),
n_estimators=1600, n_jobs=-1,
oob_score=True,
random_state=200)),
('xgboost',
XGBRegressor(base_score=None, booster=None,
colsample_bylevel=None,
colsample_bynode=None,
colsample_bytree=None, gamma=None,
gpu_id=None, importance_type='gain',
interaction_constraints=N...
n_estimator=900, n_estimators=100,
n_jobs=-1, num_parallel_tree=None,
random_state=200, reg_alpha=None,
reg_lambda=None,
scale_pos_weight=None,
subsample=None, tree_method=None,
validate_parameters=None,
verbosity=None)),
('catboost',
<catboost.core.CatBoostRegressor object at 0x7f4377a6ea90>)],
final_estimator=RandomForestRegressor(n_estimators=600,
oob_score=True,
random_state=200))
In [169]:
reg.score(X_test,Y_test)
Out[169]:
0.9148900896305339
In [170]:
preds=reg.predict(pd.get_dummies(testX))
In [171]:
result=pd.DataFrame(data={'Employee ID':test['Employee ID'],'Burn Rate':preds})
In [172]:
result.to_csv('stack.csv',index=False)